Create core library using high priority operators

A smaller core library is created using a subset of the operators.
Changed the structure of filelist.json in order to include more
information about the kernels and make the selection easier.

Resolves: COMPMID-4514

Change-Id: I079ca7d8e64346174eebdd13b834e1dd4dc36ca2
Signed-off-by: Michalis Spyrou <michalis.spyrou@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5786
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 10 insertions, 9 deletions

diff --git a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp
index 92000bb2f6..46551553c9 100644
--- a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp
@@ -48,7 +48,8 @@ namespace
 {
 struct BatchNormalizationSelectorData
 {
-    DataType dt;
+    DataType       dt;
+    const CPUInfo &ci;
 };
 using BatchNormalizationSelectorPtr = std::add_pointer<bool(const BatchNormalizationSelectorData &data)>::type;
 using BatchNormalizationKernelPtr   = std::add_pointer<void(ITensor *, ITensor *, const ITensor *, const ITensor *, const ITensor *, const ITensor *,
@@ -63,19 +64,19 @@ struct BatchNormalizationKernel
 
 static const BatchNormalizationKernel available_kernels[] =
 {
-#if defined(ENABLE_SVE)
+#if defined(ARM_COMPUTE_ENABLE_SVE)
     {
         "fp16_sve_batch_normalization",
-        [](const BatchNormalizationSelectorData & data) { return data.dt == DataType::F16; },
+        [](const BatchNormalizationSelectorData & data) { return data.dt == DataType::F16 && data.ci.has_sve(); },
         REGISTER_FP16_SVE(arm_compute::cpu::fp16_sve_batch_normalization)
     },
     {
         "f32_sve_batch_normalization",
-        [](const BatchNormalizationSelectorData & data) { return data.dt == DataType::F32; },
+        [](const BatchNormalizationSelectorData & data) { return data.dt == DataType::F32 && data.ci.has_sve(); },
         REGISTER_FP32_SVE(arm_compute::cpu::fp32_sve_batch_normalization)
     },
-#endif /* !defined(ENABLE_SVE) */
-#if defined(ENABLE_NEON)
+#endif /* !defined(ARM_COMPUTE_ENABLE_SVE) */
+#if defined(ARM_COMPUTE_ENABLE_NEON)
 #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
     {
         "fp16_neon_batch_normalization",
@@ -88,7 +89,7 @@ static const BatchNormalizationKernel available_kernels[] =
         [](const BatchNormalizationSelectorData & data) { return data.dt == DataType::F32; },
         REGISTER_FP32_NEON(arm_compute::cpu::fp32_neon_batch_normalization)
     },
-#endif /* !defined(ENABLE_NEON) */
+#endif /* !defined(ARM_COMPUTE_ENABLE_NEON) */
 };
 
 const BatchNormalizationKernel *get_implementation(const BatchNormalizationSelectorData &data)
@@ -109,7 +110,7 @@ validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const IT
 {
     ARM_COMPUTE_UNUSED(epsilon);
 
-    const auto *uk = get_implementation(BatchNormalizationSelectorData{ input->data_type() });
+    const auto *uk = get_implementation(BatchNormalizationSelectorData{ input->data_type(), CPUInfo::get() });
     ARM_COMPUTE_RETURN_ERROR_ON(uk == nullptr || uk->ukernel == nullptr);
 
     if(act_info.enabled())
@@ -387,7 +388,7 @@ void NEBatchNormalizationLayerKernel::run(const Window &window, const ThreadInfo
     }
     else
     {
-        const auto *uk = get_implementation(BatchNormalizationSelectorData{ _input->info()->data_type() });
+        const auto *uk = get_implementation(BatchNormalizationSelectorData{ _input->info()->data_type(), CPUInfo::get() });
         uk->ukernel(_input, _output, _mean, _var, _beta, _gamma, _epsilon, _act_info, window);
     }
 }